Abstract
Human world knowledge contains information about prototypical events and their participants and locations. In this paper, we train the first models using multi-task learning that can both predict missing event participants and also perform semantic role classification based on semantic plausibility. Our best-performing model is an improvement over the previous state-of-the-art on thematic fit modelling tasks. The event embeddings learned by the model can additionally be used effectively in an event similarity task, also outperforming the state-of-the-art.- Anthology ID:
- S18-2002
- Volume:
- Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11–21
- Language:
- URL:
- https://aclanthology.org/S18-2002
- DOI:
- 10.18653/v1/S18-2002
- Cite (ACL):
- Xudong Hong, Asad Sayeed, and Vera Demberg. 2018. Learning distributed event representations with a multi-task approach. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 11–21, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Learning distributed event representations with a multi-task approach (Hong et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S18-2002.pdf